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The Evolution of Visualization Technology

It’s time for a fresh look at visualization, as technological advances are making it easier to unearth the business intelligence buried in complex data sets.

Today, “visualization” refers to the innovative use of images and interactive technology to explore large, high-density datasets. Through multi-touch interfaces, mobile device views, and social network communities, many organizations are able to see, explore, and share relationships and insights in new ways. But visualization has deep roots going back to society’s earliest maps, scientific charts, and instructional illustrations – many designed to convey complex information in ways that simplify, communicate, and foster understanding. In computer science, visualization has been attempted for decades, but has been limited by graphical horsepower, CPU, memory, and storage constraints.

To help you understand how your organization can use this evolving technology, here’s a look at the current state of two types of tools:

Spreadsheets and database charts. In the past, these basic visualization tools, so familiar to many users, were restricted to essentially four variables: the x and y axes, and the size and color of plotted points. Viewpoints were typically restricted to static, siloed data. Users also had limited ability to interact with the data; there was some opportunity for drill-down, but queries and views were generally fixed.

Today’s technology offers three-dimensional visual and interactive elements that allow users to consider many variables for a given analysis. Visualization tools developers have continued to add features and toolkits—from stand-alone packages (e.g. SAS, ILOG) to productivity tool plug-ins (e.g., Excel) and cloud services (e.g. ManyEyes, Google, Tableau Public). These tools allow information acquisition (with requisite cleansing and correlation) or real-time integration to connect relevant data from within and outside of organizational boundaries. And they are highly interactive, allowing users to drill down or adjust the analysis on the fly.

Business intelligence solutions. Available business intelligence tools have required business analysts and other skilled users to perform self-guided queries and explore the data universe, demanding detailed knowledge of underlying data structures and structured query language (SQL). The primary focus has been on historical reporting, with tabular text, charts, or histograms as outputs. Without spatial and temporal context, many patterns were virtually impossible to recognize. What’s more, computational and storage bottlenecks restricted the complexity of analysis and the size of data sets.

Today’s business intelligence solutions are often part of rich analytics suites, which include visualization tools designed with business analysts and end users in mind. Data structures are abstracted based on enterprise objects and metrics, while fourth generation (4GL) programming languages allow for “drag-and-drop” exploration. The last few years have also seen consolidation in the ERP space for business intelligence (e.g., SAP and Business Objects, Oracle and Hyperion, IBM and Cognos, SPSS). The integration of product lines has made it easier for users to feed data into a tool capable of driving visualization. Solutions on the market today also enable the creation of natural links to performance management and predictive modeling tools, allowing for not only the confirmation of intuition, but also for new discoveries and insight. Moreover, high-performance appliances, in-memory analytics solutions, cloud-based infrastructure as a service, and distributed data processing have improved the cost effectiveness of visualization analysis.

What’s the next horizon in the evolution of visualization technology? “Geospatial visualization,” which marries the broad insights available through visualization with specific types of analysis that can be performed on location-related data, is already beginning to help businesses explore quantitative and qualitative spatial relationships within large sets of structured and unstructured data, represented geographically.

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